We use several mainstream vision transformers such as SWIM [1]} BOAT [2]} and TVT [3]} with a patch size of 16×16 as the feature extractors to evaluate the effectiveness of the proposed SAFF. The number of feature extractor block \(L\)  in SAFF is set to 4 since both SWIN and BOAT contain four stages. For TVT with twelve stages, we divide its stages into four groups, corresponding to four blocks in SAFF. Pre-trained models are used for three backbones on all datasets for a fair comparison. All experiments are implemented on the PyTorch and NVIDIA Tesla K40 GPU with 12GB memory. The batch size of each domain is set to 18.
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